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Prediction of broadband shock-associated noise in static and flight conditions / Développement d'une méthode de prédiction du bruit de choc des ornières

Pas de résumé / This work aims at developing a statistical prediction method for BroadBand Shock-Associated Noise (BBSAN), following recent work from NASA and Boeing. The approach is similar to studies performed for mixing noise models.First, a methodology has been developed to compute the mean turbulent _ow _eld using the Reynolds Averaged Navier-Stokes (RANS) equations. These equations are solved with elsA, a solver developed by ONERA. Most calculations have been performed on academic configurations. An extensive test campaign has been conducted on these configurations at Ecole Centrale de Lyon (ECL), so that calculations have been thoroughly compared to measurements. Mainly, two operating conditions have been tested. The first one is a jet at Mj = 1:15. This condition is typical of a civil engine in cruise. The second operating condition is a jet at Mj = 1:35, which rather concerns military engines.An acoustic model has been developed. It uses the RANS calculation as an input to compute Power Spectrum Densities (PSDs). The intermediate version of the model does not account for refraction effects: acoustic sources are propagated to the far-field using a free field Green's function. As will be seen, this gives good results on simple configurations.The model has been extended to account for refraction effects. This is achieved by computing a Green's function tailored to the problem. A ray tracing method coupled to an adjoint approach has been used to evaluate the Green's function. The computation of the Green's function has been validated for simple cases. The Green's function calculation has been coupled to the acoustic model. PSDs including refraction effects on dual-streamjets are presented.

Identiferoai:union.ndltd.org:theses.fr/2012ECDL0060
Date13 December 2012
CreatorsHenry, Cyprien
ContributorsEcully, Ecole centrale de Lyon, Bailly, Christophe
Source SetsDépôt national des thèses électroniques françaises
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation, Text

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